In [1]:

from sklearn.ensemble import RandomForestClassifier as SklearnRF

In [2]:

from cudatree import RandomForestClassifier as CudaRF

/usr/lib/python2.7/dist-packages/nose/util.py:14: DeprecationWarning: The compiler package is deprecated and removed in Python 3.x.
  from compiler.consts import CO_GENERATOR

In [3]:

import sklearn.datasets

In [4]:

d = sklearn.datasets.fetch_covtype(); x = d['data']; y = d['target']

In [5]:

print x.shape, y.shape

(581012, 54) (581012,)

In [6]:

n = x.shape[0] / 2; xtrain = x[:n]; ytrain = y[:n]; xtest = x[n:]; ytest = y[n:]

In [19]:

skrf = SklearnRF(n_estimators = 21, n_jobs = 4)

In [20]:

cudarf = CudaRF()

In [21]:

time skrf.fit(xtrain, ytrain)

CPU times: user 0.35 s, sys: 0.66 s, total: 1.01 s
Wall time: 19.40 s

Out[21]:

RandomForestClassifier(bootstrap=True, compute_importances=None,
            criterion='gini', max_depth=None, max_features='auto',
            min_density=None, min_samples_leaf=1, min_samples_split=2,
            n_estimators=21, n_jobs=4, oob_score=False, random_state=None,
            verbose=0)

In [27]:

time cudarf.fit(xtrain, ytrain, n_trees = 21, bootstrap=False)

CPU times: user 13.90 s, sys: 0.24 s, total: 14.14 s
Wall time: 14.15 s

In [28]:

print "sklearn accuracy", np.mean(skrf.predict(xtest) ==  ytest)

sklearn accuracy 0.724614982135

In [29]:

print "cudatree accuracy", np.mean(cudarf.predict(xtest) == ytest)

cudatree accuracy 0.633735619918

In [25]:

cudarf.predict(xtest)

Out[25]:

array([2, 2, 1, ..., 3, 3, 3], dtype=int32)

In [26]:

skrf.predict(xtest)

Out[26]:

array([1, 1, 1, ..., 3, 3, 3], dtype=int32)

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